Intelligent Analysis and Modeling of the Development Trend of New Energy Electric Vehicles in China Based on Machine Learning

Abstract

Since 2011, China has actively promoted new energy electric vehicles with favorable policies, marking significant industry growth akin to the "China High Speed Rail." Using data-driven approaches like machine learning and probability statistics, we crafted models for three-level indicator weights using Analytic Hierarchy Process, correlation-based multiple regression, and ARIMA-based future predictions.

In response to Question 1, we gathered over 80 development-related datasets, cleaning and visualizing data for analysis. Complex correlation analysis was mitigated by categorizing data into three indicator levels, focusing on annual sales, policies, tech advancement, market economy, infrastructure, resources, and corporate capabilities. Weighted models revealed policy, infrastructure, and tech as top influencers, impacting new energy vehicle development.

Question 2 required extensive data collection for a 10-year forecast. Historical industry data including sales, production, policy shifts, and technological advancements was pivotal. Six indicators from Question 1 and sales volumes were integrated using TOPSIS, with predictive modeling employing correlation coefficients and machine learning models like BILSTM and SARIMA.

For Question 3, data collection and mathematical models assessed new energy vehicles' impact on traditional counterparts. Analysis indicated a negative impact, with some gasoline vehicle sales shifting but limited effect on factors like oil prices and emissions, signaling potential future trade-offs.

Question 4 involved analyzing foreign policies affecting China's new energy vehicle exports. Policy resistance variables assessed policy impacts on export volumes, with anomaly detection and ARIMA models predicting future impacts.

To gauge differences between new energy and traditional vehicles for a 1 million population city (Question 5), calculations indicated substantial resource and environmental savings from new energy vehicles.

Lastly (Question 6), leveraging these findings, an open letter advocating new energy vehicles' benefits and global contributions to the electric vehicle industry was crafted.